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Testing the robustness of deterministic models of optimal dynamic pricing and lot-sizing for deteriorating items under stochastic conditions

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  • Maryam Ghoreishi

    (BSS, Aarhus University)

Abstract

Many models within the field of optimal dynamic pricing and lot-sizing models for deteriorating items assume everything is deterministic and develop a differential equation as the core of analysis. Two prominent examples are the papers by Rajan et al. (Manag Sci 38:240–262, 1992) and Abad (Manag Sci 42:1093–1104, 1996). To our knowledge, nobody has ever tested whether the optimal solutions obtained in those papers are valid if the real system is exposed to randomness: with regard to demand process as well as with regard to the deterioration process. The motivation is that although the real world is indeed stochastic, it is often more convenient to work with a deterministic decision model providing a nice closed form solution. The crucial thing is of course whether the results derived in the deterministic setting are robust when tested in a stochastic environment. Therefore, in this paper, we will try to expose the model by Abad (1996) and Rajan et al. (1992) to stochastic inputs; however, designing these stochastic inputs such that they as closely as possible are aligned with the assumptions of those papers. We do our investigation through a numerical test where we test the robustness of the numerical results reported in Rajan et al. (1992) and Abad (1996) in a simulation model. Our numerical results seem to confirm that the results stated in these papers are indeed robust when being imposed to stochastic inputs.

Suggested Citation

  • Maryam Ghoreishi, 2019. "Testing the robustness of deterministic models of optimal dynamic pricing and lot-sizing for deteriorating items under stochastic conditions," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(4), pages 1131-1152, December.
  • Handle: RePEc:spr:cejnor:v:27:y:2019:i:4:d:10.1007_s10100-018-0538-7
    DOI: 10.1007/s10100-018-0538-7
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    References listed on IDEAS

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    1. Shukai Li & Jianxiong Zhang & Wansheng Tang, 2015. "Joint dynamic pricing and inventory control policy for a stochastic inventory system with perishable products," International Journal of Production Research, Taylor & Francis Journals, vol. 53(10), pages 2937-2950, May.
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    4. Zhang, Jianxiong & Wang, Yu & Lu, Lihao & Tang, Wansheng, 2015. "Optimal dynamic pricing and replenishment cycle for non-instantaneous deterioration items with inventory-level-dependent demand," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 136-145.
    5. Bakker, Monique & Riezebos, Jan & Teunter, Ruud H., 2012. "Review of inventory systems with deterioration since 2001," European Journal of Operational Research, Elsevier, vol. 221(2), pages 275-284.
    6. Wedad Elmaghraby & P{i}nar Keskinocak, 2003. "Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions," Management Science, INFORMS, vol. 49(10), pages 1287-1309, October.
    7. Arvind Rajan & Richard Rakesh, 1992. "Dynamic Pricing and Ordering Decisions by a Monopolist," Management Science, INFORMS, vol. 38(2), pages 240-262, February.
    8. P. L. Abad, 1996. "Optimal Pricing and Lot-Sizing Under Conditions of Perishability and Partial Backordering," Management Science, INFORMS, vol. 42(8), pages 1093-1104, August.
    9. Omar Besbes & Assaf Zeevi, 2009. "Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms," Operations Research, INFORMS, vol. 57(6), pages 1407-1420, December.
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    2. Beullens, Patrick & Ghiami, Yousef, 2022. "Waste reduction in the supply chain of a deteriorating food item – Impact of supply structure on retailer performance," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1017-1034.

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